1.
Meta-Learning
1.1.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
1.2.
HOW TO TRAIN YOUR MAML
1.3.
RAPID LEARNING OR FEATURE REUSE? TOWARDS UNDERSTANDING THE EFFECTIVENESS OF MAML
1.4.
On First-Order Meta-Learning Algorithms
1.5.
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
1.6.
Meta-Learning with Implicit Gradients
2.
Meta-RL
2.1.
Meta-Reinforcement Learning of Structured Exploration Strategies
2.2.
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
3.
Autonomous Systems
3.1.
[review]Planning and Decision-Making for Autonomous Vehicles
3.2.
[review]A Review of Mobile Robot Motion Planning Methods
3.3.
RL
3.3.1.
Asynchronous Methods for Deep Reinforcement Learning (A3C)
3.3.2.
LEARNING TO NAVIGATE IN COMPLEX ENVIRONMENTS
3.3.3.
Curiosity-driven Exploration by Self-supervised Prediction
3.3.4.
End-to-End Navigation Strategy With Deep Reinforcement Learning for Mobile Robots
3.3.5.
LEARNING TO NAVIGATE IN COMPLEX ENVIRONMENTS
3.3.6.
Learning to Navigate Through Complex Dynamic Environment With Modular Deep Reinforcement Learning
Light (default)
Rust
Coal
Navy
Ayu
RL
Reinforcement learning.